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2022 | OriginalPaper | Chapter

30. Stock Backtesting Engine Using Pairs Trading

Authors : Rahul Chauhan, Marian Sorin Nistor, Doina Bein, Stefan Pickl, Wolfgang Bein

Published in: ITNG 2022 19th International Conference on Information Technology-New Generations

Publisher: Springer International Publishing

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Abstract

The chapter introduces a Stock Backtesting Engine designed to test historical data using pairs trading strategy, a popular method in statistical arbitrage. Pairs trading involves finding stocks with similar historical price behaviors and betting on their convergence. The engine identifies cointegrated pairs using statistical methods, runs backtesting algorithms, and provides detailed analysis and visualization of trade effectiveness. The system architecture, including modules for data collection, pair finding, backtesting, and analysis, is explained. The chapter highlights the importance of cointegration tests, such as the Engle-Granger method, and demonstrates the engine's capabilities with real-world examples. Results from backtesting eight major companies are presented, showcasing the engine's potential to enhance trading strategies. The chapter concludes with limitations and future work, suggesting improvements like machine learning integration and web service conversion.
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Metadata
Title
Stock Backtesting Engine Using Pairs Trading
Authors
Rahul Chauhan
Marian Sorin Nistor
Doina Bein
Stefan Pickl
Wolfgang Bein
Copyright Year
2022
Book
ITNG 2022 19th International Conference on Information Technology-New Generations

Print ISBN: 978-3-030-97651-4

Electronic ISBN: 978-3-030-97652-1

Copyright Year: 2022

DOI
https://doi.org/10.1007/978-3-030-97652-1_30

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